Abstract: Data mining techniques are methods for obtaining useful knowledge from these large databases. One of the main tasks of data mining is association rule mining (ARM), which is used to find interesting rules from large amounts of data. A new confabulation-inspired association rule mining (CARM) algorithm is proposed using an interestingness measure inspired by cogency. Cogency is only computed based on pairwise item conditional probability, so the proposed algorithm mines association rules by only one pass through the file. The proposed algorithm is also more efficient for dealing with infrequent items due to its cogency-inspired approach. The problem of associative classification is used here for evaluating the proposed algorithm. This paper evaluates CARM over data sets. Experiments show that the proposed algorithm is consistently faster due to its one time file access and consumes less memory space than the Conditional Frequent Patterns growth algorithm. In addition, statistical analysis reveals the superiority of the approach for classifying minority classes in unbalanced data sets using dynamic transaction datasets.
Keywords: Data mining, Association rule mining, confabulation, cogency, frequent patterns item.